Globally Optimized Least-Squares Post-Filtering for Speech Enhancement

1. A computer-implemented method, comprising: receiving audio signals via a microphone array from sound sources in an environment; hypothesizing multiple sound field scenarios to generate multiple output signals, including hypothesizing a point interferer, diffuse noise, and white noise, based on the received audio signals; calculating fixed beamformer coefficients based on the received audio signals; determining covariance matrix models based on the multiple output signals; calculating a covariance matrix based on the received audio signals; estimating power of the sound sources to find a solution that minimizes the difference between the determined covariance matrix models and the calculated covariance matrix; calculating and applying post-filter coefficients based on the estimated power; and generating an output audio signal based on the received audio signals and the post-filter coefficients.

2. The method of claim 1 , wherein the multiple generated output signals are compared and the output signal with the highest signal-to-noise ratio among the multiple output generated signals is selected as the final output signal.

3. The method of claim 1 , wherein the estimating of the power is based on a Frobenius norm.

4. The method of claim 3 , wherein the Frobenius norm is computed using the Hermitian symmetry of the covariance matrices.

5. The method of claim 1 , further comprising: determining the location of at least one of the sound sources using sound-source location methods to hypothesize the sound field scenarios, determine the covariance matrix models, and calculate the covariance matrix.

6. The method of claim 1 , wherein the covariance matrix models are generated based on the plurality of hypothesized sound field scenarios.

7. The method of claim 6 , wherein a covariance matrix model is selected to maximize an objective function that reduces noise.

8. The method of claim 7 , wherein an objective function is the sample variance of the final output audio signal.

9. An apparatus, comprising: one or more processing devices and one or more storage devices storing instructions that, when executed by the one or more processing devices, cause the one or processing devices to: receive audio signals via a microphone array from sound sources in an environment; hypothesize sound field scenarios to generate multiple output signals, including hypothesizing a point interferer, diffuse noise, and white noise, based on the received audio signals; calculate fixed beamformer coefficients based on the received audio signals; determine covariance matrix models based on the multiple output signals; calculate a covariance matrix based on the received audio signals; estimate power of the sound sources to find a solution that minimizes the difference between the determined covariance matrix models and the calculated covariance matrix; calculate and applying post-filter coefficients based on the estimated power; and generate an output audio signal based on the received audio signals and the post-filter coefficients.

10. An apparatus of claim 9 , wherein the multiple generated output signals are compared and the output signal with the highest signal-to-noise ratio among the multiple output generated signals.

11. An apparatus of claim 9 , wherein the estimating of the power is based on a Frobenius norm.

12. An apparatus of claim 11 , wherein the Frobenius norm is computed using a Hermitian symmetry of the covariance matrices.

13. An apparatus of claim 9 , further comprising: determining the location of at least one of the sound sources using sound-source location methods to hypothesize the sound field scenarios, determine the covariance matrix models, and calculate the covariance matrix.

14. A non-transitory computer-readable medium, comprising sets of instructions for: receiving audio signals via a microphone array from sound sources in an environment; hypothesizing sound field scenarios to generate multiple output signals, including hypothesizing a point interferer, diffuse noise, and white noise, based on the received audio signals; calculating fixed beamformer coefficients based on the received audio signals; determining covariance matrix models based on the multiple output signals; calculating a covariance matrix based on the received audio signals; estimating power of the sound sources to find a solution that minimizes the difference between the determined covariance matrix models and the calculated covariance matrix; calculating and applying post-filter coefficients based on the estimated power; and generating an output audio signal based on the received audio signals and the post-filter coefficients.

15. A non-transitory computer-readable medium of claim 14 , wherein the multiple generated output signals are compared and the output signal with the highest signal-to-noise ratio among the multiple output generated signals.

16. A non-transitory computer-readable medium of claim 14 , wherein the estimating of the power is based on a Frobenius norm.

17. A non-transitory computer-readable medium of claim 16 , wherein the Frobenius norm is computed using a Hermitian symmetry of the covariance matrices.